code: def hybrid_forward(self, F, input_context_list):
      context_embed = [
          self.embeddings[i](input_context)
          for i, input_context in enumerate(input_context_list)
      ]
      context_input = []
      for i in context_embed:
          context_input.append(F.expand_dims(i, axis=1))
      context_embedding = F.concat(*context_input, dim=1)
      context_encoding, context_att = self.context_encoder(
          context_embedding
      )
      context_out = self.context_pooling_dp(context_encoding)
      context_out = self.dense(context_out)
  
      return context_out
  
file_path: analytics-zoo/pyzoo/zoo/models/recommendation/txt.py
imports: from mxnet import gluon
  from gluonnlp.model.transformer import TransformerEncoder, TransformerEncoderCell
label_desc: Use image node_id 2 for calling the ContextTransformer obj's underlying member method code with example usage: obj.hybrid_forward(F, input_context_list) and returns: context_out